






"""simple docstring"""
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = None
    __UpperCAmelCase           = BloomTokenizerFast
    __UpperCAmelCase           = BloomTokenizerFast
    __UpperCAmelCase           = True
    __UpperCAmelCase           = False
    __UpperCAmelCase           = """tokenizer_file"""
    __UpperCAmelCase           = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           super().setUp()
           __magic_name__      				=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer' )
           tokenizer.save_pretrained(self.tmpdirname )







    def 				a__			(		self :	List[Any]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           kwargs.update(self.special_tokens_map )
           return BloomTokenizerFast.from_pretrained(self.tmpdirname				,    **UpperCamelCase_ )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.get_rust_tokenizer()

           __magic_name__      				=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
           __magic_name__      				=    [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

           __magic_name__      				=    tokenizer.batch_encode_plus(UpperCamelCase_ )['input_ids']
           self.assertListEqual(UpperCamelCase_				,    UpperCamelCase_ )

           __magic_name__      				=    tokenizer.batch_decode(UpperCamelCase_ )
           self.assertListEqual(UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	Tuple=6 ):





           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                         __magic_name__      				=    self.rust_tokenizer_class.from_pretrained(UpperCamelCase_				,    **UpperCamelCase_ )
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         __magic_name__      				=    'This is a simple input'
                         __magic_name__      				=    ['This is a simple input 1', 'This is a simple input 2']
                         __magic_name__      				=    ('This is a simple input', 'This is a pair')
                         __magic_name__      				=    [
                             ('This is a simple input 1', 'This is a simple input 2'),
                             ('This is a simple pair 1', 'This is a simple pair 2'),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(UpperCamelCase_				,    max_length=UpperCamelCase_ )
                                tokenizer_r.encode_plus(UpperCamelCase_				,    max_length=UpperCamelCase_ )

                                tokenizer_r.batch_encode_plus(UpperCamelCase_				,    max_length=UpperCamelCase_ )
                                tokenizer_r.encode(UpperCamelCase_				,    max_length=UpperCamelCase_ )
                                tokenizer_r.batch_encode_plus(UpperCamelCase_				,    max_length=UpperCamelCase_ )
                         except ValueError:
                                self.fail('Bloom Tokenizer should be able to deal with padding' )

                         __magic_name__      				=    None  # Hotfixing padding = None
                         self.assertRaises(UpperCamelCase_				,    tokenizer_r.encode				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length' )

                         # Simple input
                         self.assertRaises(UpperCamelCase_				,    tokenizer_r.encode_plus				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length' )

                         # Simple input
                         self.assertRaises(
                             UpperCamelCase_				,    tokenizer_r.batch_encode_plus				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length'				,    )

                         # Pair input
                         self.assertRaises(UpperCamelCase_				,    tokenizer_r.encode				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length' )

                         # Pair input
                         self.assertRaises(UpperCamelCase_				,    tokenizer_r.encode_plus				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length' )

                         # Pair input
                         self.assertRaises(
                             UpperCamelCase_				,    tokenizer_r.batch_encode_plus				,    UpperCamelCase_				,    max_length=UpperCamelCase_				,    padding='max_length'				,    )







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    self.get_rust_tokenizer()
           __magic_name__      				=    load_dataset('xnli'				,    'all_languages'				,    split='test'				,    streaming=UpperCamelCase_ )

           __magic_name__      				=    next(iter(UpperCamelCase_ ) )['premise']  # pick up one data
           __magic_name__      				=    list(sample_data.values() )

           __magic_name__      				=    list(map(tokenizer.encode				,    UpperCamelCase_ ) )
           __magic_name__      				=    [tokenizer.decode(UpperCamelCase_				,    clean_up_tokenization_spaces=UpperCamelCase_ ) for x in output_tokens]
           self.assertListEqual(UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map )				,    1 )
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] )				,    1 )







"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)